docs: def load_creditscoring1(cost_mat_parameters=None):
      """Load and return the credit scoring Kaggle Credit competition dataset (classification).
  
      The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
  
      Parameters
      ----------
      cost_mat_parameters : Dictionary-like object, optional (default=None)
          If not None, must include 'int_r', 'int_cf', 'cl_max', 'n_term', 'k','lgd'
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, the interesting attributes are:
          'data', the data to learn, 'target', the classification labels,
          'cost_mat', the cost matrix of each example,
          'target_names', the meaning of the labels, 'feature_names', the
          meaning of the features, and 'DESCR', the full description of the dataset.
  
      References
      ----------
      .. [1] A. Correa Bahnsen, D.Aouada, B, Ottersten,
             "Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring",
             in Proceedings of the International Conference on Machine Learning and Applications,
             , 2014.
  
      Examples
      --------
      Let's say you are interested in the samples 10, 25, and 50
  
      >>> from costcla.datasets import load_creditscoring1
      >>> data = load_creditscoring1()
      >>> data.target[[10, 17, 400]]
      array([0, 1, 0])
      >>> data.cost_mat[[10, 17, 400]]
      array([[  1023.73054104,  18750.        ,      0.        ,      0.        ],
             [   717.25781516,   6749.25      ,      0.        ,      0.        ],
             [  1004.32819923,  17990.25      ,      0.        ,      0.        ]])
      """
      module_path = dirname(__file__)
      raw_data = pd.read_csv(join(module_path, 'data', 'creditscoring1.csv.gz'), delimiter=',', compression='gzip')
      descr = open(join(module_path, 'descr', 'creditscoring1.rst')).read()
  
      # Exclude MonthlyIncome = nan or =0 or DebtRatio >1
      raw_data = raw_data.dropna()
      raw_data = raw_data.loc[(raw_data['MonthlyIncome'] > 0)]
      raw_data = raw_data.loc[(raw_data['DebtRatio'] < 1)]
  
      target = raw_data['SeriousDlqin2yrs'].values.astype(np.int)
  
      data = raw_data.drop(['SeriousDlqin2yrs', 'id'], 1)
  
      # Calculate cost_mat (see[1])
      if cost_mat_parameters is None:
          cost_mat_parameters = {'int_r': 0.0479 / 12,
                                 'int_cf': 0.0294 / 12,
                                 'cl_max': 25000,
                                 'n_term': 24,
                                 'k': 3,
                                 'lgd': .75}
  
      pi_1 = target.mean()
      cost_mat = _creditscoring_costmat(data['MonthlyIncome'].values, data['DebtRatio'].values, pi_1, cost_mat_parameters)
  
      return Bunch(data=data.values, target=target, cost_mat=cost_mat,
                   target_names=['no', 'yes'], DESCR=descr,
                   feature_names=data.columns.values, name='CreditScoring_Kaggle2011')